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In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image. If this relationship is explicitly encoded, instead of treated as any other variation, the complexity of the problem is decreased, leading to a reduction in the size of the required model. In this paper, we propose the Rotation Equivariant Vector Field Networks (RotEqNet), a Convolutional...
The recent advances in sparse coding and dictionary learning have shown extremely good performances and robustness in high-dimensional classification problems. Most often, dictionary-based methods rely either on the reconstruction power of the dictionary or on the structure of the sparse representation. In this paper we jointly exploit the discrimination power of both approaches by combining the reconstruction...
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